简介本案例研究旨在通过采用逐步的系统方法来提高医学文本中ChatGPT-4的可追溯性和检索准确性。重点是从三个关于糖尿病酮症酸中毒(DKA)的国际指南中检索临床答案。方法建立了系统的方法来指导检索过程。每个指南都提出了一个问题,以确保准确性并保持引用。ChatGPT-4被用来检索答案,并集成了“链接阅读器”插件,以方便直接访问包含指南的网页。随后,ChatGPT-4用于编译答案,同时提供对来源的引用。每个问题重复这个过程30次,以确保一致性。在这份报告中,我们介绍了我们对检索准确性的观察,反应的一致性,以及在此过程中遇到的挑战。结果将ChatGPT-4与“链接阅读器”插件集成在一起显示了显着的可追溯性和检索准确性优势。根据分析的指南,AI模型成功提供了相关且准确的临床答案。尽管偶尔会遇到网页访问和轻微的内存漂移的挑战,集成系统的整体性能是有希望的。答案的汇编也令人印象深刻,并为进一步的审判带来了巨大的希望。结论本案例研究的结果有助于利用AI文本生成模型作为医学专业人员和研究人员的有价值的工具。本案例研究中采用的系统方法和“链接阅读器”插件的集成为自动化医学文本合成提供了一个框架,在从不同来源编译之前一次问一个问题,这提高了人工智能模型的可追溯性和检索准确性。AI模型的进一步改进和完善以及与其他软件实用程序的集成有望增强AI生成的建议在医学和科学学术界的实用性和适用性。这些进步有可能推动日常医疗实践的重大改进。
Introduction This case study aimed to enhance the traceability and retrieval accuracy of ChatGPT-4 in medical text by employing a step-by-step systematic approach. The focus was on retrieving clinical answers from three international
guidelines on diabetic ketoacidosis (DKA). Methods A systematic methodology was developed to guide the retrieval process. One question was asked per
guideline to ensure accuracy and maintain referencing. ChatGPT-4 was utilized to retrieve answers, and the \'Link Reader\' plug-in was integrated to facilitate direct access to webpages containing the
guidelines. Subsequently, ChatGPT-4 was employed to compile answers while providing citations to the sources. This process was iterated 30 times per question to ensure consistency. In this report, we present our observations regarding the retrieval accuracy, consistency of responses, and the challenges encountered during the process. Results Integrating ChatGPT-4 with the \'Link Reader\' plug-in demonstrated notable traceability and retrieval accuracy benefits. The AI model successfully provided relevant and accurate clinical answers based on the analyzed
guidelines. Despite occasional challenges with webpage access and minor memory drift, the overall performance of the integrated system was promising. The compilation of the answers was also impressive and held significant promise for further trials. Conclusion The findings of this case study contribute to the utilization of AI text-generation models as valuable tools for medical professionals and researchers. The systematic approach employed in this case study and the integration of the \'Link Reader\' plug-in offer a framework for automating medical text synthesis, asking one question at a time before compilation from different sources, which has led to improving AI models\' traceability and retrieval accuracy. Further advancements and refinement of AI models and integration with other software utilities hold promise for enhancing the utility and applicability of AI-generated recommendations in medicine and scientific academia. These advancements have the potential to drive significant improvements in everyday medical practice.